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New Robustness Measures of Communication Networks Against Virus Attacks

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Science of Cyber Security (SciSec 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11933))

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Abstract

The robustness of the communication network is an important measurement of network connectivity after some attacks, such as virus and failure. To evaluate the network robustness, many robustness measures have been presented depending on the type of attacks. These measures mainly concentrate on the relation between the robustness of the network and the number of deleted nodes, and seldom consider the robustness of the network in the scenarios that the network is attacked by the virus. The existing measures can not completely evaluate the robustness of the network against virus attacks and can not accurately reveal the relation between network robustness and the transmissibility of the virus. So, it is necessary to study the relation between the robustness of the network and the effective spreading rate of the virus, especially important for communication networks. In this paper, we first introduce three new measures based on the effective spreading rate to evaluate the robustness. Then, we further study the relation between network topology and the three measures. Our results are helpful in designing robust communication networks according to the new robustness measures.

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Acknowledgments

This research has been supported by the National Natural Science Foundation of China (Grant Nos. 61672298, 61873326, 61373136, 61802155), the Philosophy Social Science Research Key Project Fund of Jiangsu University (Grant No. 2018SJZDI142) and the Research Foundation for Humanities and Social Sciences of Ministry of Education of China (Grant Nos. 17YJAZH071).

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Correspondence to Yurong Song .

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Li, Y., Song, B., Zhang, X., Jiang, GP., Song, Y. (2019). New Robustness Measures of Communication Networks Against Virus Attacks. In: Liu, F., Xu, J., Xu, S., Yung, M. (eds) Science of Cyber Security. SciSec 2019. Lecture Notes in Computer Science(), vol 11933. Springer, Cham. https://doi.org/10.1007/978-3-030-34637-9_11

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  • DOI: https://doi.org/10.1007/978-3-030-34637-9_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34636-2

  • Online ISBN: 978-3-030-34637-9

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